Estimating excess length of stay due to healthcare-associated infections: a systematic review and meta-analysis of statistical methodology

Sarkis Manoukian, Sally Stewart, Stephanie Dancer, Nicholas Graves, Helen Mason, Agi McFarland, Chris Robertson, Jacqui Reilly

Research output: Contribution to journalReview article

4 Citations (Scopus)

Abstract

BACKGROUND: Healthcare-associated infection (HAI) affects millions of patients worldwide. HAI is associated with increased healthcare costs, owing primarily to increased hospital length of stay (LOS) but calculating these costs is complicated due to time-dependent bias. Accurate estimation of excess LOS due to HAI is essential to ensure we invest in cost-effective infection prevention and control (IPC) measures.

AIM: To identify and review the main statistical methods that have been employed to estimate differential LOS between patients with, and without, HAI; to highlight and discuss potential biases of all statistical approaches.

METHODS: A systematic review from 1997 to April 2017 was conducted in PUBMED, CINAHL, PROQUEST and ECONLIT databases. Studies were quality assessed using an adapted Newcastle-Ottawa Scale (NOS). Methods were categorised into time-fixed or time-varying with the former exhibiting time-dependent bias. We use two examples of meta-analysis to illustrate how estimates of excess LOS differ between different studies.

FINDINGS: Ninety-two studies with estimates on excess LOS were identified. The majority of articles employed time-fixed methods (75%). Studies using time-varying methods are of higher quality according to NOS. Studies using time-fixed methods overestimate additional LOS attributable to HAI. Undertaking meta-analysis is challenging due to a variety of study designs and reporting styles. Study differences are further magnified by heterogeneous populations, case definitions, causative organisms and susceptibilities.

CONCLUSIONS: Methodologies have evolved over the last 20 years but there is still a significant body of evidence reliant upon time-fixed methods. Robust estimates are required to inform investment in cost-effective IPC interventions.

LanguageEnglish
JournalJournal of Hospital Infection
Early online date11 Jun 2018
DOIs
Publication statusE-pub ahead of print - 11 Jun 2018

Fingerprint

Cross Infection
Healthcare
Infection
Excess
Meta-Analysis
Length of Stay
Methodology
Costs
Infection Control
Costs and Cost Analysis
Time-varying
Estimate
Robust Estimate
Review
Statistical method
Susceptibility
Health Care Costs
Databases
Population

Keywords

  • healthcare-associated infection
  • HAI
  • length of stay
  • cost effective
  • infection prevention and control

Cite this

Manoukian, Sarkis ; Stewart, Sally ; Dancer, Stephanie ; Graves, Nicholas ; Mason, Helen ; McFarland, Agi ; Robertson, Chris ; Reilly, Jacqui. / Estimating excess length of stay due to healthcare-associated infections : a systematic review and meta-analysis of statistical methodology. In: Journal of Hospital Infection. 2018.
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abstract = "BACKGROUND: Healthcare-associated infection (HAI) affects millions of patients worldwide. HAI is associated with increased healthcare costs, owing primarily to increased hospital length of stay (LOS) but calculating these costs is complicated due to time-dependent bias. Accurate estimation of excess LOS due to HAI is essential to ensure we invest in cost-effective infection prevention and control (IPC) measures.AIM: To identify and review the main statistical methods that have been employed to estimate differential LOS between patients with, and without, HAI; to highlight and discuss potential biases of all statistical approaches.METHODS: A systematic review from 1997 to April 2017 was conducted in PUBMED, CINAHL, PROQUEST and ECONLIT databases. Studies were quality assessed using an adapted Newcastle-Ottawa Scale (NOS). Methods were categorised into time-fixed or time-varying with the former exhibiting time-dependent bias. We use two examples of meta-analysis to illustrate how estimates of excess LOS differ between different studies.FINDINGS: Ninety-two studies with estimates on excess LOS were identified. The majority of articles employed time-fixed methods (75{\%}). Studies using time-varying methods are of higher quality according to NOS. Studies using time-fixed methods overestimate additional LOS attributable to HAI. Undertaking meta-analysis is challenging due to a variety of study designs and reporting styles. Study differences are further magnified by heterogeneous populations, case definitions, causative organisms and susceptibilities.CONCLUSIONS: Methodologies have evolved over the last 20 years but there is still a significant body of evidence reliant upon time-fixed methods. Robust estimates are required to inform investment in cost-effective IPC interventions.",
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Estimating excess length of stay due to healthcare-associated infections : a systematic review and meta-analysis of statistical methodology. / Manoukian, Sarkis; Stewart, Sally; Dancer, Stephanie; Graves, Nicholas; Mason, Helen; McFarland, Agi; Robertson, Chris; Reilly, Jacqui.

In: Journal of Hospital Infection, 11.06.2018.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Estimating excess length of stay due to healthcare-associated infections

T2 - Journal of Hospital Infection

AU - Manoukian, Sarkis

AU - Stewart, Sally

AU - Dancer, Stephanie

AU - Graves, Nicholas

AU - Mason, Helen

AU - McFarland, Agi

AU - Robertson, Chris

AU - Reilly, Jacqui

N1 - Copyright © 2018. Published by Elsevier Ltd.

PY - 2018/6/11

Y1 - 2018/6/11

N2 - BACKGROUND: Healthcare-associated infection (HAI) affects millions of patients worldwide. HAI is associated with increased healthcare costs, owing primarily to increased hospital length of stay (LOS) but calculating these costs is complicated due to time-dependent bias. Accurate estimation of excess LOS due to HAI is essential to ensure we invest in cost-effective infection prevention and control (IPC) measures.AIM: To identify and review the main statistical methods that have been employed to estimate differential LOS between patients with, and without, HAI; to highlight and discuss potential biases of all statistical approaches.METHODS: A systematic review from 1997 to April 2017 was conducted in PUBMED, CINAHL, PROQUEST and ECONLIT databases. Studies were quality assessed using an adapted Newcastle-Ottawa Scale (NOS). Methods were categorised into time-fixed or time-varying with the former exhibiting time-dependent bias. We use two examples of meta-analysis to illustrate how estimates of excess LOS differ between different studies.FINDINGS: Ninety-two studies with estimates on excess LOS were identified. The majority of articles employed time-fixed methods (75%). Studies using time-varying methods are of higher quality according to NOS. Studies using time-fixed methods overestimate additional LOS attributable to HAI. Undertaking meta-analysis is challenging due to a variety of study designs and reporting styles. Study differences are further magnified by heterogeneous populations, case definitions, causative organisms and susceptibilities.CONCLUSIONS: Methodologies have evolved over the last 20 years but there is still a significant body of evidence reliant upon time-fixed methods. Robust estimates are required to inform investment in cost-effective IPC interventions.

AB - BACKGROUND: Healthcare-associated infection (HAI) affects millions of patients worldwide. HAI is associated with increased healthcare costs, owing primarily to increased hospital length of stay (LOS) but calculating these costs is complicated due to time-dependent bias. Accurate estimation of excess LOS due to HAI is essential to ensure we invest in cost-effective infection prevention and control (IPC) measures.AIM: To identify and review the main statistical methods that have been employed to estimate differential LOS between patients with, and without, HAI; to highlight and discuss potential biases of all statistical approaches.METHODS: A systematic review from 1997 to April 2017 was conducted in PUBMED, CINAHL, PROQUEST and ECONLIT databases. Studies were quality assessed using an adapted Newcastle-Ottawa Scale (NOS). Methods were categorised into time-fixed or time-varying with the former exhibiting time-dependent bias. We use two examples of meta-analysis to illustrate how estimates of excess LOS differ between different studies.FINDINGS: Ninety-two studies with estimates on excess LOS were identified. The majority of articles employed time-fixed methods (75%). Studies using time-varying methods are of higher quality according to NOS. Studies using time-fixed methods overestimate additional LOS attributable to HAI. Undertaking meta-analysis is challenging due to a variety of study designs and reporting styles. Study differences are further magnified by heterogeneous populations, case definitions, causative organisms and susceptibilities.CONCLUSIONS: Methodologies have evolved over the last 20 years but there is still a significant body of evidence reliant upon time-fixed methods. Robust estimates are required to inform investment in cost-effective IPC interventions.

KW - healthcare-associated infection

KW - HAI

KW - length of stay

KW - cost effective

KW - infection prevention and control

UR - https://sciencedirect.com/journal/journal-of-hospital-infection

U2 - 10.1016/j.jhin.2018.06.003

DO - 10.1016/j.jhin.2018.06.003

M3 - Review article

JO - Journal of Hospital Infection

JF - Journal of Hospital Infection

SN - 0195-6701

ER -